Employing pressure signatures for personal identification

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a biometric authentication system. In one aspect, a method includes capturing pressure measurement data as a sequence of pressure maps as the user applies pressure to the pressure sensing device; determining a dynamic pressure signature for the user based on the pressure measurement data, the dynamic pressure signature including a temporal sequence of the pressure maps; and authenticating the user based on a comparison of the dynamic pressure signature to an initial dynamic pressure signature.

TECHNICAL FIELD

This specification generally relates to pressure sensor devices inbiometric identification technology.

BACKGROUND

Systems incorporating biometric identification technology uniquelyidentify users by evaluating one or more distinguishing biologicaltraits. Unique identifiers include fingerprints, hand geometry, earlobegeometry, retina and iris patterns, voice waves, DNA, and signatures.

SUMMARY

Implementations of the present disclosure are generally directed toemploy pressure sensing in a biometric system. More specifically,implementations are directed to capturing pressure measurement data as asequence of pressure maps as a user applies pressure to a pressuresensor device by, for examples, walking or standing on the surface ofpressure sensor device. A static and a dynamic pressure signature may begenerated for each user from the captured pressure measurement data inform of a temporal sequence of pressure maps. The dynamic pressuresignature being employed to authenticate the users to enable the use ofservices.

In a general implementation, systems, apparatus, and methods forauthenticating a user based on pressure measurement data includecapturing pressure measurement data as a sequence of pressure maps asthe user applies pressure to a pressure sensing device. A dynamicpressure signature is determined for the user based on the pressuremeasurement data. The dynamic pressure signature including a temporalsequence of the pressure maps. The user is authenticated based on acomparison of the dynamic pressure signature to an initial dynamicpressure signature.

In another general implementation, one or more non-transitorycomputer-readable storage media coupled to one or more processors andhaving instructions stored thereon which, when executed by the one ormore processors, cause the one or more processors to perform operationsthat include: capturing pressure measurement data as a sequence ofpressure maps as a user applies pressure to a pressure sensing device. Adynamic pressure signature is determined for the user based on thepressure measurement data. The dynamic pressure signature including atemporal sequence of the pressure maps. The user is authenticated basedon a comparison of the dynamic pressure signature to an initial dynamicpressure signature.

In yet another general implementation, a system includes a pressuresensor device, one or more processors; and a computer-readable storagedevice coupled to the one or more processors and having instructionsstored thereon which, when executed by the one or more processors, causethe one or more processors to perform operations that include capturingpressure measurement data as a sequence of pressure maps as a userapplies pressure to the pressure sensing device. A dynamic pressuresignature is determined for the user based on the pressure measurementdata. The dynamic pressure signature including a temporal sequence ofthe pressure maps. The user is authenticated based on a comparison ofthe dynamic pressure signature to an initial dynamic pressure signature.

An aspect combinable with the general implementations, the operations ormethod includes generating a similarity score based on the comparison ofthe dynamic pressure signature to the initial dynamic pressuresignature, and authenticating the user based on the similarity score.

In an aspect combinable with any of the previous aspects, the similarityscore is based on Euclidean distance of the dynamic pressure signatureand the initial dynamic pressure signature.

In an aspect combinable with any of the previous aspects, the similarityscore in included in multimodal-biometric score; and wherein the user isauthenticated based on the multimodal-biometric score.

In an aspect combinable with any of the previous aspects, the pressuremaps includes a collection of time signals derived from each pressurepixel observed by the pressure sensing device as the user appliespressure.

In an aspect combinable with any of the previous aspects, the dynamicpressure signature includes at least one of which of the user's feetpredominantly touches the ground as the user walks, a first point ofimpact on each of the users feet as the user walks, a progression ofpressure points as the user walks, and gait patterns for the user.

In an aspect combinable with any of the previous aspects, the capturedpressure measurement data includes the user's foot/show contour, anangle between the user's feet, curvature of the user's feet, gait,balance, sway, posture, weight distribution, the user's dominant foot,or ballistocardiograms.

In an aspect combinable with any of the previous aspects, the initialdynamic pressure signature was captured as the user entered into anarea.

In an aspect combinable with any of the previous aspects, the initialdynamic pressure signature was captured during an enrollment process.

In an aspect combinable with any of the previous aspects, the operationsor method includes capturing, with a camera, an image of the user,wherein a distance of the user from the camera is determined based on aposition of the pressure sensing device, wherein authenticating the useris further based on a comparison of the image of the user to an initialimage of the user captured during the enrollment process.

In an aspect combinable with any of the previous aspects, the operationsor method includes determining a static pressure signature for the userbased on the pressure measurement data; wherein authenticating the useris further based on a comparison of the static pressure signature forthe user to an initial static pressure signature captured during theenrollment process.

In an aspect combinable with any of the previous aspects, the staticpressure signature includes an approximate size and shape of the user'sfoot or shoes, the user applies pressure to the pressure sensing deviceby standing on the pressure sensing device or by walking across thepressure sensing device.

In an aspect combinable with any of the previous aspects, the pressuresensing device is embedded in flooring.

In an aspect combinable with any of the previous aspects, the operationsor method includes displaying, on a display, information to the userbased on authenticating the user.

In an aspect combinable with any of the previous aspects, the pressuresensing device comprises a communication interface; and wherein thepressure measurement data is received through the communicationinterface.

In an aspect combinable with any of the previous aspects, the pressuresensing device comprises an array of pressure sensing elements.

In an aspect combinable with any of the previous aspects, each of thepressure sensing elements comprises at least one force sensitiveresistor (FSR).

Particular implementations of the subject matter described in thisdisclosure can be implemented so as to realize one or more of thefollowing advantages. The dynamic pressure signature is a soft biometricmodality that is independent of many other biometric modalities and thusit can contribute to the corpora of identification signals in abiometric system. Further, unlike camera-based systems, a dynamicpressure signature is not affected by adverse optical circumstances. Thedynamic pressure signature can also protect users of a biometricauthentication system from some spoofing attacks. Shoe-prints may alsobe gleaned from such pressure maps and used for user identification.Since the location of user's feet are known, standoff distance estimatesbetween user and for instance a kiosk employing such scanner may also bedetermined.

It is appreciated that methods in accordance with the present disclosurecan include any combination of the aspects and features describedherein. That is, methods in accordance with the present disclosure arenot limited to the combinations of aspects and features specificallydescribed herein, but also may include any combination of the aspectsand features provided.

The details of one or more implementations of the present disclosure areset forth in the accompanying drawings and the description below. Otherfeatures and advantages of the present disclosure will be apparent fromthe description and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 depicts a kiosk machine as an example environment to which abiometric authentication system may deployed.

FIG. 2 depicts an example environment that can be employed to multiinstances of a biometric authentication system.

FIG. 3 depicts a system that can be used to implement the technologydescribed herein.

FIG. 4 is a flowchart of an example process employed within a biometricauthentication system.

FIG. 5 is a block diagram representing examples of computing devices.

DETAILED DESCRIPTION

Implementations of the present disclosure are generally directed tocapturing static and dynamic pressure signatures for biometricauthentication. More particularly, implementations of the presentdisclosure are directed to a biometric authentication system deployedwithin a kiosk type device that employs a pressure sensor surface tocapture static and dynamic pressure signatures of users. The static anddynamic pressure signatures, as well as the static “shoeprints”, may beemployed by the biometric authentication system to authenticate theusers. Services are provided, through the kiosk, to the users based onthe authentication.

Various biometric identification/authentication systems are based oncapturing biometric data that can be compared with or analyzed withreference to baseline or template date to generate amultimodal-biometric score. Biometric data refers to any computer datathat is created during a biometric process. Authentication systems mayemploy models, templates, images, maps, similarity scores and so forththat are generated based on biometric data/traits collected from/aboutusers, such as prints (e.g., fingerprints, foot prints, palm prints),retinal scans, pressure signatures (e.g., static, dynamic), face scans,gait measures, olfactory biometrics (e.g., odor), and auditorybiometrics (e.g., voice prints). All or a subset of the above biometrictraits maybe fused to provide a more accurate multimodal-biometric scorefrom the aforesaid signals to be used by itself or to help the primarybiometric modality in matching as well as anti-spoofing, since thespoofers usually attack one or few, and not all, biometric modalities ina multimodal system.

In view of the foregoing, and as described in further detail herein,implementations of the present disclosure provide for a biometricauthentication system that can be deployed within a kiosk-type device,such as an automated teller machine (ATM). In some implementations, thedescribed biometric authentication system employs designated surfacewith embedded pressure sensors, such as pressure sensing mat, to capturepressure measurement data as a user walks over or stands on the sensors.The captured pressure measurement data may include a user's foot/showcontour, angle between the feet, curvature of the feet, gait, balance,sway, posture, weight distribution, dominant foot, ballistocardiograms,and so forth.

This pressure measurement data can be analyzed to generate a baseline ortemplate pressure signature (e.g., static and/or dynamic) for each user.These baseline signatures may be generated based on a sequence ofpressure maps, which include a collection of time signals derived fromeach pressure pixel observed by the pressure sensitive surface on whicha user is standing. The pressure signatures may also be biometricallyused, for example, for a stationary user as he or she interacts with akiosk or similar device. In some implementations, a static pressuresignature includes the approximate size and shape of a user's foot,partial attributes, in case of specialty footwear, such as the width ofthe sole and an overall approximate length of the shoe/foot (e.g., ifthe user is wearing high heels), and so forth. In some implementations,the dynamic pressure signature includes how the user walks or moves,such as the area of the foot the predominantly touches the ground, thefirst point of impact on the foot, how pressure points progress, gaitpatterns, center of pressure or gravity movement signals, and so forth.Gait patterns may include, for example, repetitious sequence of limbmotions to move the body forward while simultaneously maintainingstability. These pressure signatures can be employed alone or incombination with other biometric data, such as described above, inbiometric authentication.

In some implementations, a baseline or template static and/or dynamicpressure signature can be captured during an enrollment process. Forexample, a biometric authentication system may require enrolling usersto stand or walk on a designated surface, such as a pressure mat orflooring, with embedded pressure sensors during an enrollment process tocapture a baseline dynamic pressure signature for each user. In someimplementations, the static and dynamic pressure signatures capturedduring the enrollment process may be stored on a storage deviceaccessible to the biometric authentication system. During run-time,sample (verification) static and/or dynamic pressure signatures of auser can be captured and compared with the stored static and/or dynamicpressure signature template(s) to determine if the user may beauthenticated. In some implementations, a similarity score may begenerated base on the comparison of the enrollment and verificationpressure signature templates. In some implementations, this similarityscore is based on Euclidean distance of the templates. In someimplementations, this distance metric could be cosine or Mahalanobis. Insome implementation, such similarity measure is learned by way ofexposing a machine learning paradigm, such as a neural network (staticsuch as a convolutional neural net, convolutional neural network (CNN),or dynamic such as a long short-term memory (LSTM)); or support vectormachines, Support Vector Machine (SVM), by exposing them to a largenumber of data points from positive (genuine user templates) or negative(impostor user templates). In some implementations, the input to thesesimilarity measures are the raw pressure surface data streams (such asin the case of CNN and LSTM neural networks) capable of internal featurerepresentation learning. In some implementations, such pressuresignature features are derived algorithmically before presenting them tothe similarity measure or classifiers. Examples of such features includecenter of pressure (COP) time coordinates signals and their derivativessuch as path eccentricity, path length and standard deviation, swing ofCOP values in anteroposterior (AP) and mediolateral (ML) directions,statistics of COP time signals in AP and ML directions such as maximum,mean, standard deviation, velocity, and spectral features; as well asthe area of traversed paths created by COP sways. In someimplementations, families of features (such as dynamic vs. static) areclassified separately and the classification scores are combined by wayof a fusion scheme such as (weighted) sum rule, product rule, orhierarchical fusion. In some implementations the fusion rule itself is atrainable function parametrized by way of supervised learning tomaximize the biometric accuracy of the ensemble. Other types ofbase-line comparisons may performed at run-time based on data capturedby the pressure sensors. For examples, an optical image of user's shoesmay be captured. At a low level, such images may provide data regardingthe shoe's height, length, and silhouette. At higher resolutions, thedata may include abnormalities in/on the shoes, such as, scuffs andapparitions in the shoe print. Such data may be employed in, forexample, a short term biometric identification or forensic shoe printanalysis.

FIG. 1 depicts a kiosk machine 100 as an example environment to which abiometric authentication system may deployed. The kiosk 100 includes oneor more components that support a biometric authentication system. Forexample, the kiosk 100 can include a camera 105, a display 110,illumination sources 115, and a pressure sensor 130. The display 110 maydisplay information to the user 140. The display may also be used by theuser 140 to interact with the kiosk 140. For example, the display may bea touch screen type of device displaying a user interface (UI) throughwhich the user 140 can enter and receive data.

The camera 105 can be employed to capture images of, for example, theuser 140 who is interacting with the kiosk 100. The illumination sources115 may be employed to generate electromagnetic radiation at multiplewavelengths as the camera captures image data. For example, theillumination sources 115 can each include one or more light emittingdiode (LED) elements 120 that may be controlled to generateelectromagnetic radiation patterns or sequences at different wavelengthranges. The illumination sources 115 may be spatially separated from oneanother.

The pressure sensor 130 provides a surface that can be employed by thebiometric authentication system to generate a static and/or dynamicpressure signature for the user 140. In some implementations, thepressure sensor 130 includes a matrix of individual pressure sensors,interweaved vertical and/or horizontal array of pressure sensingelements, and/or an imager below a surface whose optical propertieschange in response to pressure. In some implementations, the pressuresensor 130 is a pressure-sensing mat. Such a pressure-sensing mat caninclude a top layer and a base layer with a plurality of pressuresensing elements and between the layers. The base layer may be comprisedof materials, such as, plastic, hardwood, and polycarbonate. The toplayer may be comprised of materials such as rubber, and fabricreinforced rubber. The pressure sensing elements may be arranged as agrid (e.g., a two-dimensional array). In some implementations, thepressure sensor 130 is embedded in a surface, such as the flooringmaterial.

The included pressure sensing elements detect the pressure applied tofixed points on, for example, the top layer of the pressure mat. Anysuitable pressure sensing device can be used for the pressure sensingelements, such as electromechanical pressure sensors and piezoelectricsensors. The pressure sensing elements may include FSRs, which act asvoltage dividers that detect, for example, physical pressure, squeezing,and weight.

The kiosk machine 100 may be used for various purposes that requireauthenticating users via one or more biometric authentication processes.For example, the kiosk 100 can include an ATM that allows a user towithdraw money from a bank account. In another example, the kiosk 100may be deployed at a restaurant or a fast-food outlet, and allow a userto order and pay for food. The kiosk 100 may also be deployed at anentry point (e.g., at the gate of an arena or stadium) to authenticateentrants prior to entering the venue. For example, the kiosk 100 mayalso be deployed as a turnstile type device. In such contexts, forexample, a dynamic pressure signature for a user may be registered onentry (e.g., into a store or railway) by the user walking across thepressure sensor 130. For examples, the pressure sensors may be embeddedin the floor. In such examples, the system may also be deployed to workin conjunction with check-out scanners. The user can beidentified/authenticated at check-out (e.g., as they are scanning theitems). The user's account may be automatically debited for the items,for example, based on the authentications. Other example contextsinclude the kiosk 100 working in conjunction with an active floor. Ingeneral, the kiosk 100 may be deployed at various types of locations toauthenticate users interactively, or even without any activeparticipation of the user.

In some implementations, the pressure sensor 130 may be as large asrequired to allow the user to freely move around a respective space. Forexample, the pressure sensor 130 may cover the floor of a large field orroom (e.g., built into the flooring). In some implementations, thepressure sensor 130 may be a component of a large enclosed structure.Such an enclosed structure may be, for example, in a fixed position butrotationally movable such that it moves under the user 140 as the usermoves (e.g., on a treadmill type device). In such implementations, thepressure sensor 130 allows the user 140 to move in one or more of atleast a forward moving position, a backward moving position, a left sidemoving position, a right side moving position, and/or positionstherebetween.

Alternatively, if space is limited, the pressure sensor 130 may besmaller. In such implementations, the user 140 may be required to movein a bounded area as he/she interacts with the kiosk 100. For example,the user 140 may be require to make serval passes on the pressure sensor130 as the biometric authentication system generates a static and/or adynamic pressure signature that may be used as a base-line or forauthentication purposes.

In some implementations, the pressure sensor 130 may include acommunication interface (not shown). The communication interface can beemployed to provide digital signals to the biometric authenticationsystem deployed to the kiosk 100. In some implementations, thecommunication interface may include communication via a Universal SerialBus (USB), Bluetooth, Ethernet, wireless Ethernet, and so forth.

In some implementations, the pressure sensor 130, the captured pressuremeasurement data may be employed in conjunction with the camera 105 bythe respective biometric authentication system deploy to the kiosk 100.For example, the location of the user 140 can be determined based on aknown position/coordinates of the pressure sensors 130 (e.g., thelocation of the user's feet on a 2D pressure maps). The biometricauthentication system may use this information to estimate a standoffdistance and orientation for the user 140, which can be employed toassists in functions, such as the autofocus of the camera 105. Theposition data can also be used in image analysis. For example, thebiometric authentication system may calculate the user's 140 heightbased on the position/coordinates data and a location of the facelandmarks, such the eye pair or face, as perceived by cameras 105. Insome implementations, device to face distance gleaned from the pressuresensing mat can be checked against inter-ocular or other visualestimates of distance to detect foul play such as spoof attacks, wherefor instance the attacker may be holding a spoofing screen close to thecamera and thus triggering a mismatch between footprint and visuallymetered (e.g. inter-ocular) distance. Furthermore, during a spoofattack, a spoofer may attempt to replicate the primary (e.g. camerabased,) biometric modality and thus these soft biometric modalities (inthis case, those gleaned from static footprint analysis) may show amismatch and thus reject the spoof. Optionally, the dynamics of thefootprint pressure maps can be used as a contributing soft biometric(e.g. revealing footedness, left and right foot pressure point swaysduring interaction with, for example, a UI present on the display 110)as a behavioral signature for behavioral biometric authentication.

In some implementations, the images captured using the camera 105 alongwith the determined static and/or dynamic pressure signatures can beprocessed by the biometric authentication system toidentify/authenticate the user. In some implementations, the biometricauthentication system may extract from the images and pressuresignatures, various features, such as features derived from the face,iris, vasculature underlying the sclera of the eye, the periocularregion, the gait patterns (such as described above), and so forth, toidentify/authenticate a particular user based on matching the extractedfeatures to that of one or more template images and the pressuresignatures (static and/or dynamic) generated and stored for the user 140during an enrollment process. In some implementations, the biometricauthentication system may use a machine-learning process (e.g., a deeplearning process implemented using a deep neural network architecture)to match the user 140 to his or her stored template. In someimplementations, the machine-learning process may be implemented, atleast in part, using one or more processing devices deployed on thekiosk 100. In some implementations, the kiosk 100 may communicate withone or more remote processing devices (e.g., one or more remote servers)that implement the machine learning process (see FIG. 2).

FIG. 2 depicts an example environment 200 that can be employed toexecute and/or coordinate multi instances of the described biometricauthentication system. The example environment 200 includes network 210,a back-end system 230, and kiosk devices 222-226. The kiosk devices222-226 are substantially similar to the kiosk device 100 of FIG. 1.

In some implementations, the network 210 includes a local area network(LAN), wide area network (WAN), the Internet, or a combination thereof,and connects computing devices (e.g., the kiosk devices 222-226) andback-end systems (e.g., the back-end system 230). In someimplementations, the network 210 can be accessed over a wired and/or awireless communications link.

In the depicted example, the back-end system 230 includes at least oneserver system 232 and a data store 234. In some implementations, theback-end system 230 provides access to one or more computer-implementedservices with which the kiosks 222-226 may interact. Thecomputer-implemented services may be hosted on, for example, the atleast one server system 232 and the data store 234. Thecomputer-implemented services may include, for example, anauthentication service that may be used by the kiosks 222-226 toauthenticate a user based on collected pressure signatures and/or imagedata.

In some implementations, the back-end system 230 includes computersystems employing clustered computers and components to act as a singlepool of seamless resources when accessed through the network 210. Forexample, such implementations may be used in data center, cloudcomputing, storage area network (SAN), and network attached storage(NAS) applications. In some implementations, the back-end system 230 isdeployed and provides computer-implemented services through a virtualmachine(s).

FIG. 3 depicts a system 300 that can be used to implement the technologydescribed herein. The system 300 includes sensing element 305, aprocessing device 310, and a display device 315. In comeimplementations, the system 300 may be included within a kiosk, such asdescribed with reference to FIG. 1. For example, the display device 315can be the display device 110 and the pressure sensing element 305 canbe a component of the pressure sensor 130. In some implementations, thedisplay device 315 can be disposed on a mobile device, such as asmartphone, tablet computer, or an e-reader. The pressure sensingelement 305 detects pressure as it is applied (e.g., by a user walkingor standing on the pressure sensing element 305). Example types ofpressure sensing element 305 include electromechanical pressure sensorsand analog pressure sensors though other types of pressure sensingelement may be used. The pressure sensing element 305 may include FSRsto detect, for example, physical pressure, squeezing, and weight.

Outputs from the pressure sensing element 305 can be processed using oneor more processing devices 310. In some implementations, the output ofthe one or more processing devices 310 may be used to drive a displaydevice 315. The one or more processing devices 310 can be configured toprocess the outputs from the pressure sensing element 305 in variousways. In some implementations, the one or more processors 310 areconfigured to generate a static and/or a dynamic pressure signature fora user. A dynamic pressure signature may include the approximate sizeand shape of a user's foot, partial attributes, in case of specialtyfootwear, such as the width of the sole and an overall approximatelength of the shoe/foot (e.g., if the user is wearing high heels), theuser's weight, and how the user walks or moves, such as the area of thefoot the predominantly touches the ground, the first point of impact onthe foot, how pressure points progress, gait patterns, and so forth.

FIG. 4 depicts a flow diagram of an example process 400 employed withina biometric authentication system deployed on, for example, a kioskdevice, such as kiosk 100 of FIG. 1. At 402, as a user applies pressureto a pressure sensing device, pressure measurement data is captured. Insome implementations, the user applies pressure to the pressure sensingdevice by standing on the pressure sensing device. In someimplementations, the user applies pressure to the pressure sensingdevice by walking across the pressure sensing device. In someimplementations, the pressure sensing device includes a communicationinterface and the pressure measurement data is received through thecommunication interface. In some implementations, the pressure sensingdevice is embedded in flooring. In some implementations, the pressuresensing device includes an array of pressure sensing elements. In someimplementations, each of the pressure sensing elements comprises atleast one FSR. From 402, the process 400 moves to 404.

At 404, a dynamic pressure signature for the user is determined based onthe pressure measurement data. The dynamic pressure signature includinga temporal sequence of the pressure maps. In some implementations, thedynamic pressure signature includes at least one of which of the user'sfeet predominantly touches the ground as the user walks, a first pointof impact on each of the users feet as the user walks, the progressionof pressure points as the user walks, and gait patterns for the user. Insome implementations, the pressure maps includes a collection of timesignals derived from each pressure pixel observed by the pressuresensitive device as the user applies pressure. In some implementations,the captured pressure measurement data includes the user's foot/showcontour, an angle between the user's feet, curvature of the feet, gait,balance, sway, posture, weight distribution, the user's dominant foot,or ballistocardiograms. From 404, the process 400 moved to 406.

At 406, the user is authenticated based on a comparison of the dynamicpressure signature to an initial dynamic pressure signature. In someimplementations, a similarity score is generated based on the comparisonof the dynamic pressure signature to the initial dynamic pressuresignature (e.g., an enrollment template) and the user is authenticatedbased on the similarity score. In some implementations, the similarityscore in included in multimodal-biometric score and the user isauthenticated based on the multimodal-biometric score. In someimplementations, the similarity score is based on Euclidean distance ofthe dynamic pressure signature and the initial dynamic pressuresignature. In some implementations, the initial dynamic pressuresignature was captured as the user entered into an area where thesignature is employed for short-term verification within that area. Insome implementations, the initial dynamic pressure signature wascaptured during an earlier enrollment process. In some implementations,an image of the user is captured with a camera and a distance of theuser from the camera is determined based on a position of the pressuresensing device. In such implementations, the user is authenticated isfurther based on a comparison of the image of the user to an initialimage of the user captured during the enrollment process. In someimplementations, a static pressure signature for the user is determinedbased on the pressure measurement data. In such implementations, theuser is further authenticated based on a comparison of the staticpressure signature for the user to an initial static pressure signaturecaptured during the enrollment process. In some implementations, thestatic pressure signature includes an approximate size and shape of theuser's foot or shoes. In some implementations, information is displayedto the user on a display based on the authentication. From 406, theprocess 400 ends.

FIG. 5 shows an example of a computing device 500 and a mobile computingdevice 550 that are employed to execute implementations of the presentdisclosure. The computing device 500 is intended to represent variousforms of digital computers, such as laptops, desktops, workstations,personal digital assistants, servers, blade servers, mainframes, andother appropriate computers. The mobile computing device 550 is intendedto represent various forms of mobile devices, such as personal digitalassistants, cellular telephones, smart-phones, AR devices, and othersimilar computing devices. The components shown here, their connectionsand relationships, and their functions, are meant to be examples only,and are not meant to be limiting.

The computing device 500 includes a processor 502, a memory 504, astorage device 506, a high-speed interface 508, and a low-speedinterface 512. In some implementations, the high-speed interface 508connects to the memory 504 and multiple high-speed expansion ports 510.In some implementations, the low-speed interface 512 connects to alow-speed expansion port 514 and the storage device 506. Each of theprocessor 502, the memory 504, the storage device 506, the high-speedinterface 508, the high-speed expansion ports 510, and the low-speedinterface 512, are interconnected using various buses, and may bemounted on a common motherboard or in other manners as appropriate. Theprocessor 502 can process instructions for execution within thecomputing device 500, including instructions stored in the memory 504and/or on the storage device 506 to display graphical information for agraphical user interface (GUI) on an external input/output device, suchas a display 516 coupled to the high-speed interface 508. In otherimplementations, multiple processors and/or multiple buses may be used,as appropriate, along with multiple memories and types of memory. Inaddition, multiple computing devices may be connected, with each deviceproviding portions of the necessary operations (e.g., as a server bank,a group of blade servers, or a multi-processor system).

The memory 504 stores information within the computing device 500. Insome implementations, the memory 504 is a volatile memory unit or units.In some implementations, the memory 504 is a non-volatile memory unit orunits. The memory 504 may also be another form of a computer-readablemedium, such as a magnetic or optical disk.

The storage device 506 is capable of providing mass storage for thecomputing device 500. In some implementations, the storage device 506may be or include a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, a tape device, aflash memory, or other similar solid-state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. Instructions can be stored in an information carrier.The instructions, when executed by one or more processing devices, suchas processor 502, perform one or more methods, such as those describedabove. The instructions can also be stored by one or more storagedevices, such as computer-readable or machine-readable mediums, such asthe memory 504, the storage device 506, or memory on the processor 502.

The high-speed interface 508 manages bandwidth-intensive operations forthe computing device 500, while the low-speed interface 512 manageslower bandwidth-intensive operations. Such allocation of functions is anexample only. In some implementations, the high-speed interface 508 iscoupled to the memory 504, the display 516 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 510,which may accept various expansion cards. In the implementation, thelow-speed interface 512 is coupled to the storage device 506 and thelow-speed expansion port 514. The low-speed expansion port 514, whichmay include various communication ports (e.g., USB, Bluetooth, Ethernet,wireless Ethernet) may be coupled to one or more input/output devices.Such input/output devices may include a scanner 530, a printing device534, or a keyboard or mouse 536. The input/output devices may also becoupled to the low-speed expansion port 514 through a network adapter.Such network input/output devices may include, for example, a switch orrouter 532.

The computing device 500 may be implemented in a number of differentforms, as shown in the FIG. 5. For example, it may be implemented as astandard server 520, or multiple times in a group of such servers. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 522. It may also be implemented as part of a rack server system524. Alternatively, components from the computing device 500 may becombined with other components in a mobile device, such as a mobilecomputing device 550. Each of such devices may contain one or more ofthe computing device 500 and the mobile computing device 550, and anentire system may be made up of multiple computing devices communicatingwith each other.

The mobile computing device 550 includes a processor 552; a memory 564;an input/output device, such as a display 554; a communication interface566; and a transceiver 568; among other components. The mobile computingdevice 550 may also be provided with a storage device, such as amicro-drive or other device, to provide additional storage. Each of theprocessor 552, the memory 564, the display 554, the communicationinterface 566, and the transceiver 568, are interconnected using variousbuses, and several of the components may be mounted on a commonmotherboard or in other manners as appropriate. In some implementations,the mobile computing device 550 may include a camera device(s) (notshown).

The processor 552 can execute instructions within the mobile computingdevice 550, including instructions stored in the memory 564. Theprocessor 552 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. For example, theprocessor 552 may be a Complex Instruction Set Computers (CISC)processor, a Reduced Instruction Set Computer (RISC) processor, or aMinimal Instruction Set Computer (MISC) processor. The processor 552 mayprovide, for example, for coordination of the other components of themobile computing device 550, such as control of user interfaces (UIs),applications run by the mobile computing device 550, and/or wirelesscommunication by the mobile computing device 550.

The processor 552 may communicate with a user through a controlinterface 558 and a display interface 556 coupled to the display 554.The display 554 may be, for example, a Thin-Film-Transistor LiquidCrystal Display (TFT) display, an Organic Light Emitting Diode (OLED)display, or other appropriate display technology. The display interface556 may comprise appropriate circuitry for driving the display 554 topresent graphical and other information to a user. The control interface558 may receive commands from a user and convert them for submission tothe processor 552. In addition, an external interface 562 may providecommunication with the processor 552, so as to enable near areacommunication of the mobile computing device 550 with other devices. Theexternal interface 562 may provide, for example, for wired communicationin some implementations, or for wireless communication in otherimplementations, and multiple interfaces may also be used.

The memory 564 stores information within the mobile computing device550. The memory 564 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 574 may also beprovided and connected to the mobile computing device 550 through anexpansion interface 572, which may include, for example, a Single inLine Memory Module (SIMM) card interface. The expansion memory 574 mayprovide extra storage space for the mobile computing device 550, or mayalso store applications or other information for the mobile computingdevice 550. Specifically, the expansion memory 574 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, theexpansion memory 574 may be provided as a security module for the mobilecomputing device 550, and may be programmed with instructions thatpermit secure use of the mobile computing device 550. In addition,secure applications may be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or non-volatilerandom access memory (NVRAM), as discussed below. In someimplementations, instructions are stored in an information carrier. Theinstructions, when executed by one or more processing devices, such asprocessor 552, perform one or more methods, such as those describedabove. The instructions can also be stored by one or more storagedevices, such as one or more computer-readable or machine-readablemediums, such as the memory 564, the expansion memory 574, or memory onthe processor 552. In some implementations, the instructions can bereceived in a propagated signal, such as, over the transceiver 568 orthe external interface 562.

The mobile computing device 550 may communicate wirelessly through thecommunication interface 566, which may include digital signal processingcircuitry where necessary. The communication interface 566 may providefor communications under various modes or protocols, such as GlobalSystem for Mobile communications (GSM) voice calls, Short MessageService (SMS), Enhanced Messaging Service (EMS), Multimedia MessagingService (MMS) messaging, code division multiple access (CDMA), timedivision multiple access (TDMA), Personal Digital Cellular (PDC),Wideband Code Division Multiple Access (WCDMA), CDMA2000, General PacketRadio Service (GPRS). Such communication may occur, for example, throughthe transceiver 568 using a radio frequency. In addition, short-rangecommunication, such as using a Bluetooth or Wi-Fi, may occur. Inaddition, a Global Positioning System (GPS) receiver module 570 mayprovide additional navigation- and location-related wireless data to themobile computing device 550, which may be used as appropriate byapplications running on the mobile computing device 550.

The mobile computing device 550 may also communicate audibly using anaudio codec 560, which may receive spoken information from a user andconvert it to usable digital information. The audio codec 560 maylikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 550. Such sound mayinclude sound from voice telephone calls, may include recorded sound(e.g., voice messages, music files, etc.) and may also include soundgenerated by applications operating on the mobile computing device 550.

The mobile computing device 550 may be implemented in a number ofdifferent forms, as shown in FIG. 5. For example, it may be implementedthe kiosk 100 described in FIG. 1. Other implementations may include amobile device 582 and a tablet device 584. The mobile computing device550 may also be implemented as a component of a smart-phone, personaldigital assistant, AR device, or other similar mobile device.

Computing device 500 and/or 550 can also include USB flash drives. TheUSB flash drives may store operating systems and other applications. TheUSB flash drives can include input/output components, such as a wirelesstransmitter or USB connector that may be inserted into a USB port ofanother computing device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be for a special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural,object-oriented, assembly, and/or machine language. As used herein, theterms machine-readable medium and computer-readable medium refer to anycomputer program product, apparatus and/or device (e.g., magnetic discs,optical disks, memory, Programmable Logic Devices (PLDs)) used toprovide machine instructions and/or data to a programmable processor,including a machine-readable medium that receives machine instructionsas a machine-readable signal. The term machine-readable signal refers toany signal used to provide machine instructions and/or data to aprogrammable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a GUI or a web browser through which a user can interact with animplementation of the systems and techniques described here), or anycombination of such back end, middleware, or front end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, such as network 110 of FIG. 1. Examples ofcommunication networks include a LAN, a WAN, and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Although a few implementations have been described in detail above,other modifications are possible. For example, while a clientapplication is described as accessing the delegate(s), in otherimplementations the delegate(s) may be employed by other applicationsimplemented by one or more processors, such as an application executingon one or more servers. In addition, the logic flows depicted in thefigures do not require the particular order shown, or sequential order,to achieve desirable results. In addition, other actions may beprovided, or actions may be eliminated, from the described flows, andother components may be added to, or removed from, the describedsystems. Accordingly, other implementations are within the scope of thefollowing claims.

1-20. (canceled)
 21. A computer-implemented method for authenticating auser based on pressure measurement data, the method being executed byone or more processors and comprising: capturing, by a pressure sensingdevice, pressure measurement data of a user as a plurality of pressuremaps; determining a dynamic pressure signature for the user based on thepressure measurement data, the dynamic pressure signature including atemporal sequence of the pressure maps; generating a similarity scorebased on a comparison of the dynamic pressure signature to a template;and authenticating the user based on the similarity score.
 22. Thecomputer-implemented method of claim 21, wherein the similarity score isbased on Euclidean distance of the dynamic pressure signature and theinitial dynamic pressure signature.
 23. The computer-implemented methodof claim 21, wherein the similarity score in included inmultimodal-biometric score; and wherein the user is authenticated basedon the multimodal-biometric score.
 24. The computer-implemented methodof claim 21, wherein the pressure maps includes a collection of timesignals derived from each pressure pixel observed by the pressuresensing device as the user applies pressure.
 25. Thecomputer-implemented method of claim 24, wherein the dynamic pressuresignature includes at least one of: which of the user's feetpredominantly touches the ground as the user walks, a first point ofimpact on each of the users feet as the user walks, a progression ofpressure points as the user walks, and gait patterns for the user. 26.The computer-implemented method of claim 21, wherein the capturedpressure measurement data includes the user's foot/show contour, anangle between the user's feet, curvature of the user's feet, gait,balance, sway, posture, weight distribution, the user's dominant foot,or ballistocardiograms.
 27. The computer-implemented method of claim 21,wherein the template is captured at an entry-point of a pre-definedarea.
 28. The computer-implemented method of claim 21, wherein thetemplate is captured during an enrollment process.
 29. Thecomputer-implemented method of claim 28, comprising: capturing, with acamera, an image of the user, wherein a distance of the user from thecamera is determined based on a position of the pressure sensing device.30. The computer-implemented method of claim 29, wherein authenticatingthe user is further based on a comparison of the image of the user to aninitial image of the user captured during the enrollment process. 31.The computer-implemented method of claim 29, comprising: determining astatic pressure signature for the user based on the pressure measurementdata; wherein authenticating the user is further based on a comparisonof the static pressure signature for the user to an initial staticpressure signature captured during the enrollment process.
 32. Thecomputer-implemented method of claim 31, wherein the static pressuresignature includes an approximate size and shape of the user's foot orshoes.
 33. The computer-implemented method of claim 21, wherein the userapplies pressure to the pressure sensing device by standing on thepressure sensing device or by walking across the pressure sensingdevice.
 34. The computer-implemented method of claim 21, wherein thepressure sensing device is embedded in flooring.
 35. A system,comprising: a pressure sensor device; a one or more processors; and acomputer-readable storage device coupled to the one or more processorsand having instructions stored thereon which, when executed by the oneor more processors, cause the one or more processors to performoperations comprising: obtaining, from a pressure sensing device,pressure measurement data of a user as a plurality of pressure maps;determining a dynamic pressure signature for the user based on thepressure measurement data, the dynamic pressure signature including atemporal sequence of the pressure maps; generating a similarity scorebased on a comparison of the dynamic pressure signature to a template;and authenticating the user based on the similarity score.
 36. Thesystem of claim 35, comprising: a display, wherein the operationscomprise: displaying, on the display, information to the user based onauthenticating the user.
 37. The system of claim 35, wherein thepressure sensing device comprises a communication interface; and whereinthe pressure measurement data is received through the communicationinterface.
 38. The system of claim 35, wherein the pressure sensingdevice comprises an array of pressure sensing elements.
 39. The systemof claim 38, wherein each of the pressure sensing elements comprises atleast one force sensitive resistor (FSR).
 40. One or more non-transitorycomputer-readable storage media coupled to one or more processors andhaving instructions stored thereon which, when executed by the one ormore processors, cause the one or more processors to perform operationscomprising: obtaining, from a pressure sensing device, pressuremeasurement data of a user as a plurality of pressure maps; determininga dynamic pressure signature for the user based on the pressuremeasurement data, the dynamic pressure signature including a temporalsequence of the pressure maps; generating a similarity score based on acomparison of the dynamic pressure signature to a template; andauthenticating the user based on the similarity score.